國家衛生研究院 NHRI:Item 3990099045/13270
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    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/13270


    Title: Incorporating land-use regression into machine learning algorithms in estimating the spatial-temporal variation of carbon monoxide in Taiwan
    Authors: Wong, PY;Hsu, CY;Wu, JY;Teo, TA;Huang, JW;Guo, HR;Su, HJ;Wu, CD;Spengler, JD
    Contributors: National Institute of Environmental Health Sciences
    Abstract: This paper is the first of its kind to use machine learning algorithms in conjunction with a Land-use Regression (LUR) model for predicting the spatiotemporal variation of CO concentrations in Taiwan. We used daily CO concentration from 2000 to 2016 to develop model and data from 2017 to 2018 as external data to verify the model reliability. Location of temples was used as a predictor to account for Asian culturally specific sources. With the ability to capture nonlinear relationship between observations and predictions, three LUR-based machine learning algorithms were used to estimate CO concentrations, including deep neural network (DNN), random forest (RF), and extreme gradient boosting (XGBoost). The results showed that LUR-based machine-learning model (LUR-XGBoost) has the best computation efficiency and improved adjusted R2 from 0.69 to 0.85. Our studies demonstrate the ability of the LUR-based machine learning algorithms to estimate long-term spatiotemporal CO concentration variations in fine resolution.
    Date: 2021-05
    Relation: Environmental Modelling and Software. 2021 May;139:Article number 104996.
    Link to: http://dx.doi.org/10.1016/j.envsoft.2021.104996
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1364-8152&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000641409100008
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85101257052
    Appears in Collections:[Others] Periodical Articles

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